论文标题
通过吸收全球特征来改善卷积神经网络以进行故障诊断
Improving Convolutional Neural Networks for Fault Diagnosis by Assimilating Global Features
论文作者
论文摘要
深度学习技术在复杂过程的现代故障诊断中已变得突出。特别是，卷积神经网络（CNN）表现出具有吸引力的能力，可以通过将它们转换为图像来处理多元时间序列数据。但是，现有的CNN技术主要集中于从输入图像中捕获本地或多尺度功能。通常需要深入的CNN来间接提取全局特征，这对于描述从多元动力学数据转换的图像至关重要。本文提出了一种新型的局部全球CNN（LG-CNN）结构，该体系结构直接考虑了局部和全局特征以进行故障诊断。具体而言，局部功能是由传统的本地内核获得的，而全局特征是通过使用跨越图像的整个高度和宽度的一维和脂肪内核来提取的。然后，使用完全连接的层合并本地和全局特征进行分类。拟议的LG-CNN在基准田纳西州伊士曼进程（TEP）数据集上进行了验证。与传统CNN的比较表明，所提出的LG-CNN可以大大提高故障诊断性能，而无需显着提高模型的复杂性。这归因于LG-CNN所产生的局部接收场比CNN更广泛。提出的LG-CNN体系结构可以轻松扩展到其他图像处理和计算机视觉任务。
Deep learning techniques have become prominent in modern fault diagnosis for complex processes. In particular, convolutional neural networks (CNNs) have shown an appealing capacity to deal with multivariate time-series data by converting them into images. However, existing CNN techniques mainly focus on capturing local or multi-scale features from input images. A deep CNN is often required to indirectly extract global features, which are critical to describe the images converted from multivariate dynamical data. This paper proposes a novel local-global CNN (LG-CNN) architecture that directly accounts for both local and global features for fault diagnosis. Specifically, the local features are acquired by traditional local kernels whereas global features are extracted by using 1D tall and fat kernels that span the entire height and width of the image. Both local and global features are then merged for classification using fully-connected layers. The proposed LG-CNN is validated on the benchmark Tennessee Eastman process (TEP) dataset. Comparison with traditional CNN shows that the proposed LG-CNN can greatly improve the fault diagnosis performance without significantly increasing the model complexity. This is attributed to the much wider local receptive field created by the LG-CNN than that by CNN. The proposed LG-CNN architecture can be easily extended to other image processing and computer vision tasks.
